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x402_payment_fraud_detector

Read-onlyIdempotent

Risk-focused tool that analyzes x402-USDC payment transactions for fraud patterns using on-chain forensics. Takes a transaction hash or wallet address as input and returns risk scores, suspicious indicators, and historical patterns. Designed for risk management teams to quickly assess payment legitimacy. Includes keywords: fraud detection, USDC risk, blockchain forensics, transaction monitoring. pass async:true to avoid timeout.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
walletAddressNo
includeHistoryNo
amountThresholdNo
transactionHashYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesYes
warningsYes
riskScoreYes
isSuspiciousYes
sanctionsMatchNo
fraudIndicatorsNo
transactionHistoryNo

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, openWorld, and idempotent. The description adds useful context about async timeout avoidance and on-chain forensics. However, it misleadingly says 'takes a transaction hash or wallet address' while the schema requires transactionHash, which is a behavioral inconsistency not covered by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence. However, the 'Includes keywords' sentence is filler and does not add value for an AI agent, preventing a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The output schema covers return values, so that gap is acceptable. But the description leaves two parameters (includeHistory, amountThreshold) semantically undefined and the input requirement mismatch (hash vs wallet) could lead to incorrect invocation. Not complete enough for a tool with 5 parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 20% (only async has a description). The description explains async (pass true to avoid timeout) and mentions input types (transaction hash or wallet address), but it incorrectly implies walletAddress is sufficient. It omits includeHistory and amountThreshold entirely, failing to compensate fully for low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('analyzes') and resource ('x402-USDC payment transactions') with a clear goal ('fraud patterns'). It also distinguishes itself from generic sibling tools like 'fraud_detector' by focusing on x402-USDC and on-chain forensics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context: 'Designed for risk management teams to quickly assess payment legitimacy.' This implies when to use the tool, though it does not explicitly name alternatives or exclusions. The async guidance is also a usage hint.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.